Experimental Closed-Loop Control of the Three-Dimensional Turbulent Wall Jet Using a Genetic Algorithm
Bibliographic record
Abstract
Modern day environmental and economic demands make developments in active flow control more important than ever. The full potential of active flow control is realized through adaptive control with sensor feedback, or closed-loop control. Implementation of closed-loop control in real-world turbulent flows has historically faced some of the greatest engineering challenges. The presented research addresses some of these main challenges including sensor and actuator choice, spatial-temporal flow resolution, and the choice of control method. A three-dimensional turbulent wall jet at a Reynolds number of 140000 is the test bed for the flow control research. A machine learning algorithm in the form of a genetic algorithm was used to achieve the control objective; to maximize the surface area coverage of the fluid jet over the wall. The algorithm used the novel approach of a low-dimensional subset of wall pressure fluctuations off the jet centerline in feedback. A metric for real-time control evaluation was defined by open-loop control results and physical principles that drive the lateral growth of the wall jet. Using the reduced-order control metric in the machine learning control framework, the genetic algorithm found a 12% higher lateral growth than the open-loop case and caused the width to be 2.52 times greater than the uncontrolled wall jet. The adaptability of the control method and the practicality of pressure sensors in feedback makes this a promising closed-loop control approach for other active flow control cases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".